Arguments

y

Numeric vector with incomplete data

ry

Response pattern of y (TRUE=observed,
FALSE=missing)

x

ridge

The ridge penalty applied to prevent problems with multicollinearity. The default is ridge = 1e-05, which means that 0.001 percent of the diagonal is added to the cross-product. Larger ridges may result in more biased estimates. For highly noisy data (e.g. many junk variables), set ridge = 1e-06 or even lower to reduce bias. For highly collinear data, set ridge = 1e-04 or higher.

midas.kappa

Scalar. If NULL (default) then the optimal kappa gets selected automatically. Alternatively, the user may specify a scalar. Siddique and Belin 2008 find midas.kappa = 3 to be sensible.

outout

Logical. If TRUE (default) one model is estimated for each donor (leave-one-out principle). For speedup choose outout = FALSE, which estimates one model for all observations leading to in-sample predictions for the donors and out-of-sample predictions for the recipients. Mind the inappropriateness, though.

neff

FOR EXPERTS. Null or character string. The name of an existing environment in which the effective sample size of the donors for each loop (CE iterations times multiple imputations) is supposed to be written. The effective sample size is necessary to compute the correction for the total variance as originally suggested by Parzen, Lipsitz and Fitzmaurice 2005. The objectname is midastouch.neff.

debug

FOR EXPERTS. Null or character string. The name of an existing environment in which the input is supposed to be written. The objectname is midastouch.inputlist.

...

Other named arguments.

Details

Imputation of y by predictive mean matching, based on Rubin (1987, p.
168, formulas a and b) and Siddique and Belin 2008. The procedure is as follows:

Draw a bootstrap sample from the donor pool.

Estimate a beta matrix on the bootstrap sample by the leave one out principle.